A dynamic routing method and system based on LoRa Mesh ad hoc network

Abstract

The application relates to a dynamic routing method and system based on a LoRa Mesh ad hoc network, which is applied to a LoRa Mesh network containing multiple LoRa nodes and at least one sink node; the method comprises the following steps: in step S1, devices in the ad hoc network exchange information to obtain a network topology graph; in step S2, the nodes use the global network topology graph to construct a soft-min potential field based on entropy regularization for each sink node, and select the best sink node; in step S3, after the source node determines the target sink node, the source node performs hop-by-hop routing transmission based on the potential field difference of adjacent nodes; in step S4, after a non-source node receives a data packet, the non-source node judges whether to forward the data packet according to the target ID and the next hop ID of the data packet, and the data is transmitted to the public network through the sink node. Simulation results show that the application can effectively balance the network load, prolong the network life cycle, and obtain a higher packet arrival rate and a more uniform energy consumption distribution in a multi-sink scenario compared with traditional greedy and flooding routing.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a dynamic routing method and system based on LoRa Mesh self-organizing networks. Background Technology

[0002] With the rapid development of IoT technology, Low Power Wide Area Networks (LPWANs) have been widely used in fields such as water conservancy monitoring, environmental sensing, smart cities, and emergency communications. Among them, LoRa technology has become a typical LPWAN technology due to its long transmission distance, low power consumption, and low deployment cost. In practical applications, due to limitations such as terrain conditions, node density, and communication distance, a simple star network structure is difficult to meet the communication needs in large-scale and complex environments. Therefore, LoRa-based Mesh self-organizing network technology has gradually attracted attention.

[0003] In LoRa Mesh ad hoc networks, nodes are typically battery-powered and subject to strict duty cycle regulations. During network operation, nodes need to both upload their own data and potentially act as relays, leading to significant differences in load and uneven energy consumption among nodes. Therefore, achieving a low-overhead, energy-balanced, and long-term stable routing mechanism while ensuring communication reliability is a critical issue that urgently needs to be addressed in the LoRa Mesh ad hoc network field.

[0004] Existing LoRa Mesh routing solutions largely borrow from the routing concepts of traditional ad hoc networks or wireless sensor networks. Common methods include routing strategies based on minimum hop count, controlled flooding strategies based on signal strength thresholds, and tree-structured single-aggregation routing protocols. While these solutions are simple to implement, they typically rely on a single metric for routing decisions, failing to adequately consider multi-dimensional factors such as link quality, remaining node energy, and duty cycle load. This can easily lead to some nodes being frequently selected as relay nodes, resulting in problems such as premature energy depletion, link congestion, or duty cycle exceeding limits, thus affecting the availability and lifespan of the entire network. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a dynamic routing method and system based on LoRa Mesh self-organizing network, which realizes multi-indicator-aware routing decision and supports adaptive selection of multiple aggregation nodes, so as to improve the overall reliability and life cycle of the network.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a dynamic routing method based on LoRa Mesh self-organizing network, applied to a LoRa Mesh network containing multiple LoRa nodes and at least one aggregation node, comprising the following steps:

[0007] Step S1: Devices, i.e. nodes, in the ad hoc network exchange information and use multi-index link cost to model the network topology.

[0008] Step S2: The nodes use the global network topology graph to construct a soft-min potential field based on entropy regularization for each sink node, and select the best sink node.

[0009] Step S3: After the source node determines the target aggregation node, it performs hop-by-hop routing based on the potential field difference between adjacent nodes.

[0010] Step S4: After receiving the data packet, the non-source node determines whether to forward the data packet based on the destination ID and next-hop ID of the data packet, until the data is transmitted to the public network through the aggregation node.

[0011] In a preferred embodiment, step S1 includes the following steps:

[0012] Step S11: Nodes in the ad hoc network propagate node status information within the network by sending first type of broadcast packets. The first type of broadcast packets allow multi-hop forwarding in the ad hoc network and are used to achieve the sharing of node status across the entire network. The first type of broadcast packets include at least the following fields: node unique identifier ID, node current received signal strength or signal-to-noise ratio information, node status identifier indicating whether it has Internet connectivity, timestamp and sequence number used to distinguish between new and old information, node current duty cycle load information, node current remaining energy information, and neighbor node list field used to carry the node's confirmed one-hop neighbor information.

[0013] Step S12: To obtain the "neighbor node list field" in the first type of broadcast packet, a second type of broadcast packet is introduced for one-hop neighbor discovery and link reachability confirmation; the second type of broadcast packet is set to allow only one-hop propagation and not multi-hop forwarding, and it includes at least the node unique identifier ID and the data packet sequence number field; each node sends the second type of broadcast packet at a preset period;

[0014] A node is confirmed as a stable neighbor node only if it successfully receives Type II broadcast packets from the same node multiple times within a preset time window.

[0015] Step S13: After completing the exchange of information between the first type of broadcast packets and the second type of broadcast packets, each node simultaneously obtains the status information of the nodes in the network and the neighbor relationship information between the nodes.

[0016] Given a weighted graph Vertex set The corresponding node and edge set in the network The corresponding reachable single-hop LoRa link; for any directed edge The link cost is defined as the “overhead” that is used to characterize the data sent from node i to node j. The link cost takes into account three key indicators: link signal quality RSSI, the remaining energy of the next hop node, and the current duty cycle load of the next hop node.

[0017] Step S14: Suppose that the link signal quality RSSI estimated by node j when receiving data packets from node i is... The threshold for the lowest acceptable link signal quality RSSI is denoted as . The original link signal quality RSSI is mapped to the signal quality degradation degree in the interval [0,1]. A larger value indicates a worse link; a sigmoid function is used for non-linear mapping.

[0018] (1)

[0019] Among them, parameters Control function at threshold The nearby steep terrain;

[0020] Step S15: Let the current remaining battery percentage of node j be... ,in Indicates full charge. Indicates that the battery is depleted; set an energy threshold. The mapping function is:

[0021] (2)

[0022] parameter The steepness of the control function near the threshold; this mapping function will also Energy degradation mapped to the [0,1] interval It exhibits characteristics of an S-function;

[0023] Step S16: Select a convex power function as the duty cycle penalty mapping:

[0024] (3)

[0025] in, Let $j$ be the duty cycle cost for node $j$. Let j be the duty cycle of node j. , is a convexity control parameter used to adjust the degree of nonlinear change in duty cycle cost;

[0026] Step S17: Obtain the three types of degradation indicators , , Then, adopting a method based on The norm-based fusion method combines the three types of degradation indicators into a single link cost. Specifically defined as:

[0027] (4)

[0028] in , , These are the weighting coefficients for three categories of indicators: signal quality, node energy, and duty cycle. The norm order in the fusion; , , These are three categories of degradation indicators. , , The norm;

[0029] Step S18 , , Adjust according to the usage scenario; increase the value in scenarios requiring high reliability. and ;

[0030] Step S19: Absorb the jump penalty into each edge and define the extended edge weight:

[0031] (5)

[0032] in To absorb the extended edge weight after the jump penalty, For each link edge It has comprehensive costs, This represents the hop count penalty coefficient, thus defining the path. The total cost can be written as a simple linear sum:

[0033] (6)

[0034] in, Representing a path Total routing cost.

[0035] In a preferred embodiment, after receiving the first type of broadcast packet, each node in step S11 performs the following processing:

[0036] Step S111: Determine the age of the information based on the timestamp or serial number, and only process and store the latest information;

[0037] Step S112: Store the node state information carried in the first type of broadcast packet into the local node state table for subsequent routing cost calculation;

[0038] Step S113: When the forwarding conditions are met, continue to forward the first type of broadcast packet into the network to realize the diffusion of node status throughout the ad hoc network;

[0039] Through the propagation of the first type of broadcast packets, each node in the ad hoc network obtains global status information of other nodes in the network, including attributes such as the energy level, duty cycle load, and whether each node has Internet access capability.

[0040] In a preferred embodiment, after receiving the second type of broadcast packet, the neighboring node in step S12 performs the following operation:

[0041] Step S121: Record the sender node ID and confirm that the node is a one-hop neighbor of itself;

[0042] Step S122: Based on the signal measurement results when receiving the second type of broadcast packet, determine whether the link meets the minimum communication conditions;

[0043] Step S123: Add the confirmed one-hop neighbor node to the local neighbor list and record the effective time of the neighbor relationship.

[0044] In a preferred embodiment, step S2 includes the following steps:

[0045] Step S21: Construct the potential field using a soft-min approach; for each convergence point... The potential energy value of node i Defined as:

[0046] (7)

[0047] in: It is used to control the trade-off between shortest path performance and multipath performance; All reachable neighbors of the node; This represents the extended edge weight between node i and node j after absorbing the hop count penalty;

[0048] Step S22: Soft-Min is a smooth approximation of the hard minimum operation, obtaining an approximate minimum cost by aggregating the costs of multiple candidate paths; based on the Soft-Min form, the potential energy of the node with respect to each convergence point is calculated, and for the convergence point... Define the candidate path cost of node i when it is forwarded through its reachable neighbor node j as:

[0049] (8)

[0050] in, This indicates that node i reaches the sink point via its neighbor node j. The cost of the candidate path;

[0051] The costs of each candidate path are aggregated using a soft-min approach to obtain the cost of node i with respect to the convergence point. Potential energy:

[0052] (9)

[0053] remember:

[0054] (10)

[0055] Then we have:

[0056] (11)

[0057] Therefore, when As it gradually increases, Gradually approaching the minimum candidate path cost ;when When taking a finite value, all candidate paths participate in the potential energy calculation, and the smaller the cost of the candidate path, the greater its impact on the potential energy calculation result. Thus, while prioritizing the selection of low-cost paths, consideration is retained for other better candidate paths.

[0058] Step S23: Define the potential field vector for node i. for:

[0059] (12)

[0060] Each node has potential energy evaluation in different sink directions; the source node u automatically selects the sink point with the lowest potential energy.

[0061] (13)

[0062] in This indicates the convergence point selected by node u. This represents the potential energy value of node u.

[0063] In a preferred embodiment, step S3 includes the following steps:

[0064] Step S31: After the potential field is constructed, the target convergence point is... Define the decision cost of node i for its neighbor j. for:

[0065] (14)

[0066] in Indicates the target convergence point The potential energy value;

[0067] Step S32: Use the softmax step-by-step decision-making strategy to determine the probability of selecting the next hop. Set as:

[0068] (15)

[0069] in, This indicates that node i can reach the target convergence point via neighbor node k. The decision cost during forwarding, where k is the reachable neighbor index of node i.

[0070] In a preferred embodiment, step S4 includes the following steps:

[0071] Step S41: After receiving the data packet, the non-source node first reads the next-hop node identifier in the data packet and compares the next-hop node identifier with its own node identifier;

[0072] Step S42: When the next-hop node identifier is consistent with the node identifier, it indicates that the data packet needs to be forwarded by this node. This node determines the new next-hop node identifier based on the potential field information maintained locally, and sends the updated data packet to the determined next-hop node.

[0073] Step S43: When the next-hop node identifier is inconsistent with the self-node identifier, it indicates that the data packet does not belong to the forwarding range of this node, and this node will not forward the data packet.

[0074] Through a determination mechanism based on the next-hop node identifier, only nodes that meet the forwarding conditions participate in data forwarding in the network;

[0075] Step S44: The data packet is transmitted in the network along a predetermined route in a hop-by-hop forwarding manner. When the data packet arrives at the target aggregation node, the aggregation node parses the data packet and forwards it to the public network or upper-layer application server through a wired network, cellular communication network or Internet Protocol network.

[0076] This invention also provides a dynamic routing system based on LoRa Mesh ad hoc network, including a processor, a memory, and a bus. The memory stores machine-readable instructions executed by the processor. When the system is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the dynamic routing method based on LoRa Mesh ad hoc network is as described above.

[0077] Compared with existing technologies, this invention has the following advantages: its path cost model can simultaneously reflect link quality, node status, and path hop count; the soft-min potential field introduces adjustable multi-path attributes while ensuring near-shortest path characteristics, combining optimality and robustness; the potential field vector allows multi-convergence point selection to be naturally integrated into a unified framework, avoiding the overhead of frequent route reconstruction in traditional protocols; the softmax hop-by-hop strategy enables routing to have energy balancing capabilities, significantly improving the lifespan of the LoRa network; routing implementation only requires storing a small amount of neighbor information and potential field values, and the data packets only carry convergence point identifiers, greatly reducing control overhead and meeting the actual needs of LoRa ultra-low bandwidth scenarios. Attached Figure Description

[0078] Figure 1 This is a network topology diagram in an embodiment of the present invention;

[0079] Figure 2 These are the two types of broadcast packets in the proposed solution of this invention embodiment;

[0080] Figure 3 This is a flowchart of the potential field construction and hop-by-hop routing process in the proposed solution of this invention.

[0081] Figure 4 This invention provides a comparison of packet arrival rates between the proposed scheme and common routing algorithms in this embodiment.

[0082] Figure 5 To compare the packet arrival rate of the proposed scheme with common routing algorithms under high interference channels;

[0083] Figure 6 To compare the energy consumption of the proposed scheme with the flooding routing algorithm in high-interference channels;

[0084] Figure 7 To compare the energy consumption of the proposed scheme with the greedy routing algorithm in high-interference channels;

[0085] Figure 8 To compare the energy consumption of the proposed scheme with the greedy routing algorithm in this embodiment of the invention under high interference channels and when the node is initially at full energy. Detailed Implementation

[0086] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0087] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0088] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations according to this application; as used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise; furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0089] like Figure 1-3 As shown, this embodiment provides a dynamic routing algorithm design based on LoRa Mesh self-organizing networks, including the following steps:

[0090] Step S1: Devices in the ad hoc network exchange information and use multi-index link cost to model the network topology.

[0091] Step S2: The nodes use the global network topology graph to construct a soft-min potential field based on entropy regularization for each sink node, and select the best sink node.

[0092] Step S3: After the source node determines the target aggregation node, it performs hop-by-hop routing based on the potential field difference between adjacent nodes.

[0093] Step S4: After receiving the data packet, the non-source node determines whether to forward the data packet based on the destination ID and next-hop ID of the data packet, until the data is transmitted to the public network through the aggregation node.

[0094] Further, step S1 includes the following steps:

[0095] Step S11: In one embodiment of the present invention, nodes in an ad hoc network propagate node status information across the network by sending a first type of broadcast packet. This first type of broadcast packet allows for multi-hop forwarding within the ad hoc network, enabling the sharing of node status across the entire network. The first type of broadcast packet includes at least the following fields: a unique node identifier (ID); the node's current received signal strength or signal-to-noise ratio; a status indicator indicating whether the node has internet connectivity; a timestamp and sequence number to distinguish between new and old information; the node's current duty cycle load information; the node's current remaining energy information; and a neighbor node list field to carry the node's confirmed one-hop neighbor information. Upon receiving the first type of broadcast packet, each node performs the following processing:

[0096] Step S111: Determine the age of the information based on the timestamp or serial number, and only process and store the latest information;

[0097] Step S112: Store the node state information carried in the broadcast packet into the local node state table for subsequent routing cost calculation;

[0098] Step S113: When the forwarding conditions are met, continue to forward the first type of broadcast packet into the network to realize the diffusion of node status throughout the ad hoc network.

[0099] Through the propagation of the first type of broadcast packets, each node in the ad hoc network can obtain global status information of other nodes in the network, including attributes such as the energy level, duty cycle load, and whether each node has Internet access capability.

[0100] Step S12: To obtain the "neighbor node list" from the first type of broadcast packet, this invention further introduces a second type of broadcast packet for one-hop neighbor discovery and link reachability confirmation. The second type of broadcast packet is configured to allow only one-hop propagation and does not perform multi-hop forwarding; it includes at least the node's unique identifier ID and data packet sequence number. Each node sends the second type of broadcast packet at a preset period. After receiving the second type of broadcast packet, neighboring nodes perform the following operations:

[0101] Step S121: Record the sender node ID and confirm that the node is a one-hop neighbor of itself;

[0102] Step S122: Based on the signal measurement results when the broadcast packet is received, determine whether the link meets the minimum communication conditions;

[0103] Step S123: Add the confirmed one-hop neighbor node to the local neighbor list and record the effective time of the neighbor relationship.

[0104] In one embodiment, a node is confirmed as a stable neighbor node only if it successfully receives second-type broadcast packets from the same node multiple times within a preset time window, thereby improving the reliability of neighbor relationships.

[0105] Step S13: After completing the exchange of information between the first type of broadcast packets and the second type of broadcast packets, each node can simultaneously obtain the state information of nodes in the network and the neighbor relationship information between nodes. This invention abstracts the LoRa Mesh network as a directed weighted graph from a graph theory perspective. Vertex set The corresponding node and edge set in the network The corresponding reachable single-hop LoRa link; for any directed edge The link cost is defined as the "overhead" that characterizes the data transmission from node i to node j. This cost considers three key metrics: Link Signal Quality (RSSI); Remaining energy of the next-hop node; and Current Duty Cycle Load of the next-hop node.

[0106] Unlike traditional linear normalization modeling, this invention introduces nonlinear mapping functions for three types of indicators, imposing stronger penalties on "critical intervals" and "extremely unfavorable states," making the routing algorithm more sensitive to bad links, low-power nodes, and high-duty-cycle nodes, thus better adapting to the constrained environment of LoRa Mesh.

[0107] Step S14: Suppose that the RSSI estimated by node j when receiving data packets from node i is... (Unit: dBm). The "minimum acceptable RSSI" threshold for the link is denoted as [insert threshold here]. To enhance the ability to identify the "critical RSSI region", the original RSSI is mapped to the signal quality degradation degree within the interval [0,1]. A larger value indicates a worse link. This invention uses an S-shaped logic function (Sigmoid) for nonlinear mapping:

[0108] (1)

[0109] Among them, parameters Control function at threshold The nearby steep slope

[0110] Step S15: In battery-powered LoRa nodes, remaining power is a key factor determining network lifetime and routing sustainability. Let the current remaining power percentage of node j be... ,in Indicates full charge. This indicates that the battery is depleted. Clearly, the routing algorithm should avoid nodes with low battery levels as much as possible to prevent them from going offline prematurely and causing local network fragmentation.

[0111] To implement a "reinforced penalty" for low-energy nodes, this invention introduces a logical mapping function centered on a certain energy threshold. The energy threshold is set. (For example (corresponding to 30% battery level), the mapping function is:

[0112] (2)

[0113] parameter The steepness of the control function near the threshold. This function will also... Energy degradation mapped to the [0,1] interval It exhibits characteristics of an S-function.

[0114] Step S16: Duty cycle reflects the transmission load ratio of a node within the current regulatory period and is a unique and extremely important constraint factor in LoRa networks. Unlike signal quality and remaining energy, duty cycle does not have a definite physical critical point, but rather exhibits a characteristic where the risk gradually increases with increasing load and deteriorates sharply when approaching its upper limit. This risk pattern naturally conforms to the mathematical behavior of convex functions; therefore, this invention selects a convex power function as the penalty mapping for duty cycle:

[0115] (3)

[0116] in Let j be the duty cycle of node j. , is the convexity control parameter.

[0117] Step S17: Obtain the three types of degradation indicators , , Subsequently, the present invention adopts a method based on The norm fusion form combines them into a single link cost. Specifically defined as:

[0118] (4)

[0119] in , , These are the weighting coefficients for three categories of indicators: signal quality, node energy, and duty cycle. Let be the norm order in the fusion.

[0120] Step S18 , , It's not a fixed value; it will be adjusted based on the usage scenario. For example, self-organizing networks deployed in remote areas need to prioritize lifespan, so the energy weighting coefficient needs to be increased. In scenarios where reliability is critical, the capacity must be increased. and .

[0121] Step S19: Since each additional hop introduces latency, duty cycle overhead, and packet loss probability, the hop count penalty is absorbed into each edge, defining the extended edge weight:

[0122] (5)

[0123] in This represents the hop count penalty coefficient, thus defining the path. The total cost can be written as a simple linear summation:

[0124] (6)

[0125] Further, step S2 includes the following steps:

[0126] Step S21: To avoid problems such as "single-path congestion and energy imbalance" caused by traditional hard shortest paths, this invention uses a soft-min (LogSumExp) form to construct the potential field. For each convergence point... The potential energy value of node i is defined as:

[0127] (7)

[0128] in: Managing the trade-off between "shortest path" and "multi-path" properties; This represents all reachable neighbors of the node.

[0129] Step S22: Soft-Min is a smooth approximation of the hard minimum operation, obtaining an approximate minimum cost by aggregating the costs of multiple candidate paths; based on the Soft-Min form, the potential energy of the node with respect to each convergence point is calculated, and for the convergence point... Define the candidate path cost of node i when it is forwarded through its reachable neighbor node j as:

[0130] (8)

[0131] in, This indicates that node i reaches the sink point via its neighbor node j. The cost of the candidate path;

[0132] The costs of each candidate path are aggregated using a soft-min approach to obtain the cost of node i with respect to the convergence point. Potential energy:

[0133] (9)

[0134] remember:

[0135] (10)

[0136] Then we have:

[0137] (11)

[0138] Therefore, when As it gradually increases, Gradually approaching the minimum candidate path cost ;when When taking a finite value, all candidate paths participate in the potential energy calculation, and the smaller the cost of the candidate path, the greater its impact on the potential energy calculation result. Thus, while prioritizing the selection of low-cost paths, consideration is retained for other better candidate paths.

[0139] Step S23: Define the potential vector for node i as follows:

[0140] (12)

[0141] Each node has potential energy evaluation in different sink directions. The source node u automatically selects the sink point with the lowest potential energy.

[0142] (13)

[0143] in This indicates the aggregation point selected by node u. This method of direct comparison through potential fields has the following advantages: no structural advantage of RREQ / RREP; automatic traffic contention and load balancing between sinks; natural migration of potential fields when network status changes, without the need to rebuild the tree; and support for deployment of multiple service types and multiple aggregation points.

[0144] Further, step S3 includes the following steps:

[0145] Step S31: After the potential field is constructed, nodes do not need to store the complete path; they only need to perform hop-by-hop forwarding based on the incremental cost of local neighbors. For the target aggregation point... Define the decision cost of node i for its neighbor j as:

[0146] (14)

[0147] Step S32: In a simple case, a deterministic decision can be adopted, that is, selecting the neighbor with the minimum incremental cost as the next hop. However, in order to enhance energy balance and load distribution capabilities, this invention further adopts a softmax hop-by-hop decision strategy, setting the probability of selecting the next hop as:

[0148] (15)

[0149] Its mechanism is consistent with the soft minimum idea in potential fields, where the lower-cost neighbor obtains the dominant probability, while the slightly less desirable neighbor still has a chance to be selected, thus making the path traffic distribution in the network smoother, which helps to extend node lifespan, alleviate duty cycle bottlenecks, and enhance the ability to resist random fluctuations in links.

[0150] Further, step S4 includes the following steps:

[0151] Step S41: After receiving the data packet, the non-source node first reads the next-hop node identifier in the data packet and compares the next-hop node identifier with its own node identifier.

[0152] Step S42: When the next-hop node identifier is consistent with the node identifier, it indicates that the data packet needs to be forwarded by this node. This node determines the new next-hop node identifier based on the potential field information maintained locally, and sends the updated data packet to the determined next-hop node.

[0153] Step S43: When the next-hop node identifier is inconsistent with the self-node identifier, it indicates that the data packet does not belong to the forwarding range of this node, and this node will not forward the data packet, thereby avoiding unnecessary broadcasting and redundant forwarding.

[0154] Through the above-mentioned determination mechanism based on the next-hop node identifier, only nodes that meet the forwarding conditions participate in data forwarding in the network, which significantly reduces redundant communication and duty cycle consumption in the LoRa Mesh network.

[0155] Step S44: The data packet is transmitted in the network along a predetermined route in a hop-by-hop forwarding manner. When the data packet arrives at the target aggregation node, the aggregation node parses the data packet and forwards it to the public network or upper-layer application server through a wired network, cellular communication network or Internet Protocol network.

[0156] Experimental simulation

[0157] To verify the effectiveness and applicability of the proposed routing scheme in LoRa Mesh networks, this paper constructs a multi-node LoRa self-organizing network simulation environment based on the NS-3 simulation platform. In the simulation scenario, nodes are randomly deployed within a given area, and the network includes several ordinary nodes and multiple aggregation nodes. Data transmission between nodes is completed through multi-hop communication. All nodes use the same physical layer and MAC layer parameter settings, including bandwidth, spreading factor, transmit power, and receive sensitivity, to ensure fairness in the comparison of different routing strategies. Based on this, the proposed dynamic routing scheme (SoftMinPotential Field), the greedy routing scheme based on minimum hop count (Greedy), and the flooding routing scheme (Flood) are implemented respectively. Under the same service load and simulation duration, the network performance is compared and analyzed.

[0158] Figure 4The simulation is conducted on a network with 20 nodes. Under channel conditions considering only distance path loss, the PDR of the three schemes shows a significant increasing trend with the increase of the transmission interval: when the transmission interval increases from 60s to 240–420s, the network air interface load decreases, and collision and half-duplex busy reception events decrease, causing the PDR of the Soft Min potential field routing and the shortest hop greedy approach to rapidly increase to above 0.95; when the transmission interval further increases to 780–960s, the PDR of the two unicast schemes basically reaches 1.0, indicating that under stable link conditions without random fading, link reachability is mainly determined by geometric topology, and the impact of routing strategy on reliability is significantly weakened. In contrast, the flooding scheme consistently has a lower PDR than the unicast scheme across the entire range. This is because its redundant forwarding leads to higher channel occupancy and self-interference risk, and even under low load, there is still unnecessary competition, thus limiting the upper limit of the PDR.

[0159] To simulate scenarios with higher loads and more drastic channel environment changes, a network simulation with 30 nodes was conducted. Shadowing fading was introduced in addition to distance fading, causing random fluctuations in link received power. Network reliability was affected not only by load but also by the probability of "edge links falling below the sensitivity threshold." The results are as follows... Figure 5 The results show that as the transmission interval increases, the PDR of all three schemes rises, but the magnitude of the increase and the upper limit vary significantly. The Soft Min potential field routing scheme maintains a higher PDR than the shortest hop greedy scheme in the low-to-medium load range, indicating that potential field decision-making can avoid some critical links and locally optimal paths in multi-convergence scenarios, thus achieving more robust end-to-end transmission reliability under fading conditions. The flooding scheme's PDR further increases when the transmission interval is large, potentially exceeding that of the unicast scheme. This is because its inherent multi-path redundancy increases the probability of "at least one path successfully arriving"; however, this reliability gain usually comes at the cost of a significantly increased forwarding load, making it more suitable as a reliability upper limit baseline than the optimal choice for low-power, long-term operation.

[0160] Figure 6 and Figure 7 This demonstrates the impact of different routing strategies on node energy consumption distribution under the condition that all nodes have the same initial energy. Both strategies are consistent with the network topology and parameter settings. Figure 5 The transmission intervals in both sets of experiments were consistent, both lasting 600 seconds. Figure 6 The simulation duration is 36000s. Figure 7 The simulation duration is 360,000 seconds. Figure 6 Compared with the flooding routing, the flooding routing significantly increases the energy consumption of most nodes due to a large number of redundant forwarding and repeated reception. The solution of this invention effectively reduces the total sending and receiving overhead through targeted quasi-unicast forwarding, thus making it more energy-efficient overall. Figure 7A comparison of the proposed solution with the greedy strategy reveals that the greedy strategy is more likely to create hotspot relays on the fixed shortest path, resulting in higher energy consumption and faster energy depletion at some nodes. This could potentially cause nodes with low initial energy to go offline due to power exhaustion. The proposed solution, through potential field guidance and softmax traffic distribution, distributes the forwarding load more evenly across multiple feasible paths, demonstrating better energy balance and a lower risk of premature exhaustion of critical nodes. This balance is achieved through… Figure 8 The energy consumption is more evenly distributed, as can be clearly seen from the present invention. This not only reduces the risk of link breakage caused by the premature depletion of critical nodes, but also provides support for long-term stable operation in complex terrain scenarios. However, this balance is achieved by "detouring" through potential energy sensing, so in terms of total energy consumption, the present invention's solution is slightly higher than that of the greedy algorithm.

[0161] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0162] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0163] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0164] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0165] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A dynamic routing method based on LoRa Mesh self-organizing networks, characterized in that, For application in a LoRa Mesh network containing multiple LoRa nodes and at least one aggregation node, the following steps are included: Step S1: Devices, i.e. nodes, in the ad hoc network exchange information and use multi-index link cost to model the network topology. Step S2: The nodes use the global network topology graph to construct a soft-min potential field based on entropy regularization for each sink node, and select the best sink node. Step S3: After the source node determines the target aggregation node, it performs hop-by-hop routing based on the potential field difference between adjacent nodes. Step S4: After receiving the data packet, the non-source node determines whether to forward the data packet based on the destination ID and next-hop ID of the data packet, until the data is transmitted to the public network through the aggregation node.

2. The dynamic routing method based on LoRa Mesh self-organizing network according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Nodes in the ad hoc network propagate node status information within the network by sending first type of broadcast packets. The first type of broadcast packets allow multi-hop forwarding in the ad hoc network and are used to achieve the sharing of node status across the entire network. The first type of broadcast packets include at least the following fields: node unique identifier ID, node current received signal strength or signal-to-noise ratio information, node status identifier indicating whether it has Internet connectivity, timestamp and sequence number used to distinguish between new and old information, node current duty cycle load information, node current remaining energy information, and neighbor node list field used to carry the node's confirmed one-hop neighbor information. Step S12: To obtain the "neighbor node list field" in the first type of broadcast packet, a second type of broadcast packet is introduced for one-hop neighbor discovery and link reachability confirmation; the second type of broadcast packet is set to only allow one-hop propagation and not perform multi-hop forwarding, and it includes at least the node unique identifier ID and the data packet sequence number field; each node sends the second type of broadcast packet at a preset period; A node is confirmed as a stable neighbor node only if it successfully receives Type II broadcast packets from the same node multiple times within a preset time window. Step S13: After completing the exchange of information between the first type of broadcast packets and the second type of broadcast packets, each node simultaneously obtains the status information of the nodes in the network and the neighbor relationship information between the nodes. Given a weighted graph Vertex set The corresponding node and edge set in the network The corresponding reachable single-hop LoRa link; for any directed edge The link cost is defined as the "overhead" that is used to characterize the data sent from node i to node j. The link cost takes into account three key indicators: link signal quality RSSI, the remaining energy of the next hop node, and the current duty cycle load of the next hop node. Step S14: Suppose that the link signal quality RSSI estimated by node j when receiving data packets from node i is... The threshold for the lowest acceptable link signal quality RSSI is denoted as . The original link signal quality RSSI is mapped to the signal quality degradation degree in the interval [0,1]. A larger value indicates a worse link; a sigmoid function is used for non-linear mapping. （1） Among them, parameters Control function at threshold The nearby steep terrain; Step S15: Let the current remaining battery percentage of node j be... ,in Indicates full charge. Indicates that the battery is depleted; set an energy threshold. The mapping function is: （2） parameter The steepness of the control function near the threshold; this mapping function will also Energy degradation mapped to the [0,1] interval It exhibits characteristics of an S-function; Step S16: Select a convex power function as the duty cycle penalty mapping: （3） in, Let $j$ be the duty cycle cost for node $j$. Let j be the duty cycle of node j. , is a convexity control parameter used to adjust the degree of nonlinear change in duty cycle cost; Step S17: Obtain the three types of degradation indicators , , Then, adopting a method based on The norm-based fusion method combines the three types of degradation indicators into a single link cost. Specifically defined as: （4） in , , These are the weighting coefficients for three categories of indicators: signal quality, node energy, and duty cycle. The norm order in the fusion; , , These are three categories of degradation indicators. , , The norm; Step S18 , , Adjust according to the usage scenario; increase the value in scenarios requiring high reliability. and ; Step S19: Absorb the jump penalty into each edge and define the extended edge weight: （5） in To absorb the extended edge weight after the jump penalty, For each link edge It has comprehensive costs, This represents the hop count penalty coefficient, thus defining the path. The total cost can be written as a simple linear sum: （6） in, Representing a path Total routing cost.

3. The dynamic routing method based on LoRa Mesh self-organizing network according to claim 2, characterized in that, In step S11, after receiving the first type of broadcast packet, each node performs the following processing: Step S111: Determine the age of the information based on the timestamp or serial number, and only process and store the latest information; Step S112: Store the node state information carried in the first type of broadcast packet into the local node state table for subsequent routing cost calculation; Step S113: When the forwarding conditions are met, continue to forward the first type of broadcast packet into the network to realize the diffusion of node status throughout the ad hoc network; Through the propagation of the first type of broadcast packets, each node in the ad hoc network obtains global status information of other nodes in the network, including attributes such as the energy level, duty cycle load, and whether each node has Internet access capability.

4. The dynamic routing method based on LoRa Mesh self-organizing network according to claim 2, characterized in that, In step S12, after receiving the second type of broadcast packet, the neighboring node performs the following operations: Step S121: Record the sender node ID and confirm that the node is a one-hop neighbor of itself; Step S122: Based on the signal measurement results when receiving the second type of broadcast packet, determine whether the link meets the minimum communication conditions; Step S123: Add the confirmed one-hop neighbor node to the local neighbor list and record the effective time of the neighbor relationship.

5. The dynamic routing method based on LoRa Mesh self-organizing network according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Construct the potential field using a soft-min approach; for each convergence point... The potential energy value of node i Defined as: （7） in: It is used to control the trade-off between shortest path performance and multipath performance; All reachable neighbors of the node; This represents the extended edge weight between node i and node j after absorbing the hop count penalty; Step S22: Soft-Min is a smooth approximation of the hard minimum operation, obtaining an approximate minimum cost by aggregating the costs of multiple candidate paths; based on the Soft-Min form, the potential energy of the node with respect to each convergence point is calculated, and for the convergence point... Define the candidate path cost of node i when it is forwarded through its reachable neighbor node j as: （8） in, This indicates that node i reaches the sink point via its neighbor node j. The cost of the candidate path; The costs of each candidate path are aggregated using a soft-min approach to obtain the cost of node i with respect to the convergence point. Potential energy: （9） remember: （10） Then we have: （11） Therefore, when As it gradually increases, Gradually approaching the minimum candidate path cost ;when When taking a finite value, all candidate paths participate in the potential energy calculation, and the smaller the cost of the candidate path, the greater its impact on the potential energy calculation result. Thus, while prioritizing the selection of low-cost paths, consideration is retained for other better candidate paths. Step S23: Define the potential field vector for node i. for: （12） Each node has potential energy evaluation in different sink directions; the source node u automatically selects the sink point with the lowest potential energy. （13） in This indicates the convergence point selected by node u. This represents the potential energy value of node u.

6. The dynamic routing method based on LoRa Mesh self-organizing network according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: After the potential field is constructed, the target convergence point is... Define the decision cost of node i for its neighbor j. for: （14） in Indicates the target convergence point The potential energy value; Step S32: Use the softmax step-by-step decision-making strategy to determine the probability of selecting the next hop. Set as: （15） in, This indicates that node i can reach the target convergence point via neighbor node k. The decision cost during forwarding, where k is the reachable neighbor index of node i.

7. The dynamic routing method based on LoRa Mesh self-organizing network according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: After receiving the data packet, the non-source node first reads the next-hop node identifier in the data packet and compares the next-hop node identifier with its own node identifier; Step S42: When the next-hop node identifier is consistent with the node identifier, it indicates that the data packet needs to be forwarded by this node. This node determines the new next-hop node identifier based on the potential field information maintained locally, and sends the updated data packet to the determined next-hop node. Step S43: When the next-hop node identifier is inconsistent with the self-node identifier, it indicates that the data packet does not belong to the forwarding range of this node, and this node will not forward the data packet. Through a determination mechanism based on the next-hop node identifier, only nodes that meet the forwarding conditions participate in data forwarding in the network; Step S44: The data packet is transmitted in the network along a predetermined route in a hop-by-hop forwarding manner. When the data packet arrives at the target aggregation node, the aggregation node parses the data packet and forwards it to the public network or upper-layer application server through a wired network, cellular communication network or Internet Protocol network.

8. A dynamic routing system based on a LoRa Mesh self-organizing network, comprising a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executed by the processor; characterized in that, When the system is running, the processor and the memory communicate via a bus, and the machine-readable instructions are executed by the processor as described in any one of claims 1 to 7, which is a dynamic routing method based on LoRa Mesh self-organizing network.

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